marketing-assistant-ai/README.md

Marketing Assistant AI

A comprehensive AI-powered tool designed to streamline the process of ideation, copywriting, and marketing campaign creation for Adriana James. This system generates high-quality marketing content that maintains brand consistency while significantly reducing content creation time.

🌟 Features

• AI-Powered Content Generation: Create marketing content for various channels with customizable parameters
• Brand Voice Consistency: Ensure all content adheres to Adriana James' unique brand tone and style
• Vector Search: Find similar past content for reference and inspiration
• Content Improvement: Refine generated content based on specific feedback
• Brand Style Management: Configure and update brand guidelines through a user-friendly interface
• Training Data Management: Add successful marketing content to improve the AI over time
• Content Performance Analysis: Get insights into the potential effectiveness of your content
• History Tracking: Keep track of all generated content and user interactions
• Pagination Support: Efficiently browse through large datasets of historical content
• Error Handling: Robust error handling and user feedback systems

📋 Project Overview

The Marketing Assistant AI combines a powerful backend built with FastAPI and a clean, intuitive frontend interface. It leverages advanced AI models through the Cohere API for text generation, embeddings, and reranking.

Tech Stack

• Backend: Python with FastAPI
• AI Models: Cohere's generation and embedding models
• Vector Database: FAISS for efficient content similarity search
• Storage:
  • JSON files for historical marketing data
  • SQLite for training data
  • FAISS vector store for content similarity search

🚀 Getting Started

Prerequisites

• Python 3.9+
• Node.js and npm (optional, for development tools)
• Cohere API key

Installation

1. Clone the repository

git clone http://23.29.118.76:3000/task/marketing-assistant-ai.git
cd marketing-assistant-ai

2. Set up the backend

cd backend
pip install -r requirements.txt

3. Configure environment variables

Create a .env file in the project root with the following variables:

# API Keys
COHERE_API_KEY=your-api-key-here

# LLM Configuration
LLM_MODEL=llm-model-name # e.g., command
LLM_API_KEY=your-api-key-here # if using a custom model

# Server Configuration
API_HOST=localhost
API_PORT=8000

# Database Configuration
DATABASE_URL=sqlite:///./data/training_data.db

# Brand Configuration
BRAND_NAME=Adriana James

4. Start the backend server

cd backend
python main.py

The API will be available at http://localhost:8000

📁 Project Structure

Marketing_Assistant_AI/
│-- backend/
│   │-- main.py                # FastAPI application
│   │-- copywriter.py          # AI content generation
│   │-- vector_store.py        # FAISS vector database
│   │-- embeddings.py          # Cohere embeddings
│   │-- brand_style.py         # Brand style management
│   │-- config.py              # Configuration settings
│   │-- requirements.txt       # Python dependencies
│
│-- data/
│   │-- past_campaigns/        # Stored marketing campaigns
│   │-- user_queries/          # Query history
│   │-- style_guidelines/      # Brand style config
│   │   │-- brand_style.json   # Default style guidelines
│
│-- docs/
│   │-- README.md              # Developer documentation
│   │-- API_Documentation.md   # API reference
│
│-- .env                       # Environment variables
│-- .gitignore                 # Git ignore rules
│-- LICENSE                    # License information
│-- README.md                  # This file

🔄 API Endpoints

The backend provides several RESTful API endpoints:

Endpoint	Method	Description
/generate-copy	POST	Generate marketing content
/brand-style	GET	Retrieve brand style guidelines
/brand-style	PUT	Update brand style guidelines
/training-data	POST	Add new training data
/training-data	GET	List available training data
/training-data/{id}	GET	Get specific training document
/training-data/{id}	DELETE	Delete training document
/improve-content	POST	Improve content based on feedback
/analyze-content	POST	Analyze content performance

For detailed API documentation, see API Documentation.

💡 How It Works

1. Content Generation Process

   • User submits a content request
   • System formats the prompt with brand style guidelines
   • Similar past content is retrieved from the vector database
   • The AI generates content using the formatted prompt and references
   • The system checks alignment with brand guidelines
   • Final content is returned with alignment score and suggestions

2. Brand Style Management

   • Brand style guidelines are stored in JSON format
   • The system uses these guidelines to format prompts for the AI
   • Content is checked against taboo words and preferred terminology
   • An alignment score is calculated based on adherence to guidelines

3. Vector Search

   • Marketing content is converted to vector embeddings using Cohere
   • FAISS enables efficient similarity search across thousands of documents
   • Similar content is reranked based on relevance to the query

🛠️ Development

Backend Development

The backend is built with FastAPI and follows a modular architecture:

• main.py: API endpoints and request handling
• copywriter.py: Core AI content generation
• vector_store.py: Vector database operations
• embeddings.py: Embedding generation and reranking
• brand_style.py: Brand style management
• config.py: Configuration settings

🔍 Future Enhancements

• User authentication and role-based access
• Content performance analytics dashboard
• A/B testing capabilities
• Integration with marketing platforms
• Custom model fine-tuning for even better results
• Mobile application

👥 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👏 Acknowledgements

• Cohere for AI models
• FastAPI for the API framework
• FAISS for vector search

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Made with ❤️ for Adriana James